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Recent advances in RNA sequencing (RNA-Seq) have enabled the discovery of novel transcriptomic variations that are not possible with traditional microarray-based methods. Tissue and cell specific transcriptome changes during pathophysiological stress in disease cases versus controls and in response to therapies are of particular interest to investigators studying cardiometabolic diseases. Thus, knowledge on the relationships between sequencing depth and detection of transcriptomic variation is needed for designing RNA-Seq experiments and for interpreting results of analyses. Using deeply sequenced Illumina HiSeq 2000 101 bp paired-end RNA-Seq data derived from adipose of a healthy individual before and after systemic administration of endotoxin (LPS), we investigated the sequencing depths needed for studies of gene expression and alternative splicing (AS). In order to detect expressed genes and AS events, we found that ∼100 to 150 million (M) filtered reads were needed. However, the requirement on sequencing depth for the detection of LPS modulated differential expression (DE) and differential alternative splicing (DAS) was much higher. To detect 80% of events, ∼300 M filtered reads were needed for DE analysis whereas at least 400 M filtered reads were necessary for detecting DAS. Although the majority of expressed genes and AS events can be detected with modest sequencing depths (∼100 M filtered reads), the estimated gene expression levels and exon/intron inclusion levels were less accurate. We report the first study that evaluates the relationship between RNA-Seq depth and the ability to detect DE and DAS in human adipose. Our results suggest that a much higher sequencing depth is needed to reliably identify DAS events than for DE genes.
© 2013 LIU et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Liu, Y., Ferguson, J. F., Xue, C., Silverman, I. M., Gregory, B. D., Reilly, M. P., & Li, M. (2013). Evaluating the Impacts of Sequencing Depth on Transcriptome Profiling in Human Adipose. PLoS One, 8 (6), http://dx.doi.org/10.1371/journal.pone.0066883
Additional FilesTable 1_Evaluating the Impact of Sequencing Depth.pdf (240 kB)
Figure 1_Evaluating the Impact of Sequencing Depth.pdf (130 kB)
Figure 2_Evaluating the Impact of Sequencing Depth.pdf (132 kB)
Figure 3_Evaluating the Impact of Sequencing Depth.pdf (114 kB)
Table 2_Evaluating the Impact of Sequencing Depth.pdf (206 kB)
Date Posted: 14 July 2017
This document has been peer reviewed.